Exploration of Vancouver Trees

Author : Muntakim Rahman   UBC Student Number : 71065221

Introduction

This notebook will be conducting an analysis for the Vancouver Trees dataset located in the small_unique_vancouver.csv file.

Import Packages

import numpy as np
import pandas as pd
import altair as alt
import datetime as dt

from tree_functions import *

vancouver_df = pd.read_csv('small_unique_vancouver.csv', index_col = 0)
display(vancouver_df.head())

	std_street	on_street	species_name	neighbourhood_name	date_planted	diameter	street_side_name	genus_name	assigned	civic_number	plant_area	curb	tree_id	common_name	height_range_id	on_street_block	cultivar_name	root_barrier	latitude	longitude
10747	W 20TH AV	W 20TH AV	PLATANOIDES	Riley Park	2000-02-23	28.5	EVEN	ACER	N	66	15	Y	21421	NORWAY MAPLE	4	0	NaN	N	49.252711	-123.106323
12573	W 18TH AV	W 18TH AV	CALLERYANA	Arbutus-Ridge	1992-02-04	6.0	ODD	PYRUS	N	2323	7	Y	129645	CHANTICLEER PEAR	2	2300	CHANTICLEER	N	49.256350	-123.158709
29676	ROSS ST	ROSS ST	NIGRA	Sunset	NaN	12.0	ODD	PINUS	N	7855	7	Y	154675	AUSTRIAN PINE	4	7800	NaN	N	49.213486	-123.083254
8856	DOMAN ST	DOMAN ST	AMERICANA	Killarney	1999-11-12	11.0	EVEN	FRAXINUS	N	6938	7	Y	180803	AUTUMN APPLAUSE ASH	4	6900	AUTUMN APPLAUSE	N	49.220839	-123.036721
21098	EAST BOULEVARD	EAST BOULEVARD	HIPPOCASTANUM	Shaughnessy	NaN	15.5	ODD	AESCULUS	Y	5295	N	Y	74364	COMMON HORSECHESTNUT	4	5200	NaN	N	49.238514	-123.154958

print(f''''There are {len(vancouver_df)} entries in the dataset.''')

'There are 5000 entries in the dataset.

Observe Outputs

Let's start by getting an understanding of the data sparsity (i.e. NULL values), as well as the column distributions.

display(vancouver_df.info())

<class 'pandas.core.frame.DataFrame'>
Int64Index: 5000 entries, 10747 to 7450
Data columns (total 20 columns):
 #   Column              Non-Null Count  Dtype  
---  ------              --------------  -----  
 0   std_street          5000 non-null   object 
 1   on_street           5000 non-null   object 
 2   species_name        5000 non-null   object 
 3   neighbourhood_name  5000 non-null   object 
 4   date_planted        2363 non-null   object 
 5   diameter            5000 non-null   float64
 6   street_side_name    5000 non-null   object 
 7   genus_name          5000 non-null   object 
 8   assigned            5000 non-null   object 
 9   civic_number        5000 non-null   int64  
 10  plant_area          4950 non-null   object 
 11  curb                5000 non-null   object 
 12  tree_id             5000 non-null   int64  
 13  common_name         5000 non-null   object 
 14  height_range_id     5000 non-null   int64  
 15  on_street_block     5000 non-null   int64  
 16  cultivar_name       2658 non-null   object 
 17  root_barrier        5000 non-null   object 
 18  latitude            5000 non-null   float64
 19  longitude           5000 non-null   float64
dtypes: float64(3), int64(4), object(13)
memory usage: 820.3+ KB

None

Data Sparsity

There are NULL occurrences in the date_planted, plant_area, cultivar_name columns. Let's keep these for now to visualize the data in the entries without NULL values.

Non-Numeric Data

objects_df = vancouver_df.describe(include = 'object').T
display(objects_df)

	count	unique	top	freq
std_street	5000	603	CAMBIE ST	52
on_street	5000	607	CAMBIE ST	49
species_name	5000	171	SERRULATA	463
neighbourhood_name	5000	22	Renfrew-Collingwood	384
date_planted	2363	1599	2004-02-16	7
street_side_name	5000	4	ODD	2554
genus_name	5000	67	ACER	1218
assigned	5000	2	N	4564
plant_area	4950	38	10	736
curb	5000	2	Y	4593
common_name	5000	361	KWANZAN FLOWERING CHERRY	383
cultivar_name	2658	176	KWANZAN	383
root_barrier	5000	2	N	4679

Observing the data stored as objects, there seem to be variation in distinct values for given columns.

The std_street and on_street column have greater than 600 distinct values and would not be good candidates for the EDA.

Looking at the date_planted column, it seems that there are only 1599 distinct values in the entire dataset. This would entail repeated dates across the entries, which is rather interesting.

The curb and root_barrier columns are binary in nature and should be one-hot encoded in our final analysis.

Numeric Data

numeric_df = vancouver_df.describe(include = np.number).T
display(numeric_df)

	count	mean	std	min	25%	50%	75%	max
diameter	5000.0	12.340888	9.266600	0.000000	4.000000	10.000000	18.000000	71.000000
civic_number	5000.0	2975.707600	2078.580429	2.000000	1300.500000	2639.000000	4123.000000	9113.000000
tree_id	5000.0	128682.584600	75412.260406	36.000000	61321.500000	130130.500000	191332.000000	270750.000000
height_range_id	5000.0	2.734400	1.569570	0.000000	2.000000	2.000000	4.000000	9.000000
on_street_block	5000.0	2960.227000	2086.861052	0.000000	1300.000000	2600.000000	4100.000000	9100.000000
latitude	5000.0	49.247349	0.021251	49.202783	49.230152	49.247981	49.263275	49.293930
longitude	5000.0	-123.107128	0.049137	-123.220560	-123.144178	-123.105861	-123.063484	-123.023311

Observing the data stored as type np.number, there seem to be differences in std deviation for given columns.

Based on the std deviation of 75412.260406, the tree_id column probably includes data for a unique identifier. We can use this to identify our trees, but it doesn't serve much other use for our EDA.

There is a very large std deviation for the civic_number column, with the min value being 2 and the max being 9113. There is similar behavior in the on_street_block column, which very similar mean, min, and max values to civic_number. I'm not particularly interested in these columns, but we can visualize the correlation.

The height_range_id column has a mean value, as well as a 25th and 50th percentile ~2 which is interesting. I'd like to see the distribution of this column.

The latitude and longitude column have a std deviation less than 0.1, which would entail most trees being in the same vicinity. We can try using this data to see where trees are densely concentrated.

Questions of Interest

We want to explore this dataset to understand :

• What trees are commonly found in Vancouver?
• Where are trees located in Vancouver?
• How big are these trees?
• When were these trees planted?

Columns of Interest

We are going to be visualizing the data in the following columns :

• genus_name
• latitude
• longitude
• neighbourhood_name
• height_range_id
• diameter
• date_planted

vancouver_df = vancouver_df[
    [
        'latitude', 'longitude', 'neighbourhood_name',
        'genus_name', 
        'height_range_id', 'diameter', 'plant_area',
        'date_planted'
    ]
]

display(vancouver_df.head())
display(vancouver_df.tail())

	latitude	longitude	neighbourhood_name	genus_name	height_range_id	diameter	plant_area	date_planted
10747	49.252711	-123.106323	Riley Park	ACER	4	28.5	15	2000-02-23
12573	49.256350	-123.158709	Arbutus-Ridge	PYRUS	2	6.0	7	1992-02-04
29676	49.213486	-123.083254	Sunset	PINUS	4	12.0	7	NaN
8856	49.220839	-123.036721	Killarney	FRAXINUS	4	11.0	7	1999-11-12
21098	49.238514	-123.154958	Shaughnessy	AESCULUS	4	15.5	N	NaN

	latitude	longitude	neighbourhood_name	genus_name	height_range_id	diameter	plant_area	date_planted
6132	49.221161	-123.061023	Victoria-Fraserview	PRUNUS	2	17.0	9	NaN
5642	49.241544	-123.070644	Kensington-Cedar Cottage	CORNUS	1	3.0	10	2014-01-14
8777	49.224511	-123.048723	Killarney	LIRIODENDRON	2	3.5	7	2002-04-15
23489	49.259208	-123.096905	Mount Pleasant	DAVIDIA	1	5.5	5	2003-12-02
7450	49.243772	-123.078967	Kensington-Cedar Cottage	ACER	1	3.0	8	NaN

Data Transformation

Prior to visualizing the dataset, we will be assigning the decade_planted column to provide more meaning to the time periods in which trees were planted. This will also enable us to implement a decade_planted filter to our visualizations.

vancouver_df = vancouver_df.assign(
    decade_planted = vancouver_df['date_planted'].apply(
        lambda x : f'''{(dt.datetime.strptime(x, '%Y-%m-%d').year // 10) * 10}s''' if x == x else np.nan
    )
)

display(vancouver_df.head())
display(vancouver_df.tail())

	latitude	longitude	neighbourhood_name	genus_name	height_range_id	diameter	plant_area	date_planted	decade_planted
10747	49.252711	-123.106323	Riley Park	ACER	4	28.5	15	2000-02-23	2000s
12573	49.256350	-123.158709	Arbutus-Ridge	PYRUS	2	6.0	7	1992-02-04	1990s
29676	49.213486	-123.083254	Sunset	PINUS	4	12.0	7	NaN	NaN
8856	49.220839	-123.036721	Killarney	FRAXINUS	4	11.0	7	1999-11-12	1990s
21098	49.238514	-123.154958	Shaughnessy	AESCULUS	4	15.5	N	NaN	NaN

	latitude	longitude	neighbourhood_name	genus_name	height_range_id	diameter	plant_area	date_planted	decade_planted
6132	49.221161	-123.061023	Victoria-Fraserview	PRUNUS	2	17.0	9	NaN	NaN
5642	49.241544	-123.070644	Kensington-Cedar Cottage	CORNUS	1	3.0	10	2014-01-14	2010s
8777	49.224511	-123.048723	Killarney	LIRIODENDRON	2	3.5	7	2002-04-15	2000s
23489	49.259208	-123.096905	Mount Pleasant	DAVIDIA	1	5.5	5	2003-12-02	2000s
7450	49.243772	-123.078967	Kensington-Cedar Cottage	ACER	1	3.0	8	NaN	NaN

Analysis

Q1 : What trees are commonly found in Vancouver?

Let's plot the count of each genus_name to visualize the most and least common trees within the city. Let's trim down the genus_name visualized to include the 10 most and 10 least common trees.

genera_df = vancouver_df['genus_name'].value_counts() \
.sort_values(ascending = False) \
.to_frame() \
.assign(total_trees = len(vancouver_df))

genera_df = genera_df.reset_index()
genera_df.columns = ['genus_name', 'number_of_trees', 'total_trees']

display(genera_df.head())

	genus_name	number_of_trees	total_trees
0	ACER	1218	5000
1	PRUNUS	1050	5000
2	FRAXINUS	238	5000
3	TILIA	238	5000
4	QUERCUS	218	5000

fig_num = 1

most_common_plot, fig_num = get_genera_plot(
    effective_df = vancouver_df, 
    subtitle = 'Most Common Vancouver Tree Genera',
    most_common = True, 
    fig_num = fig_num
)

least_common_plot, _ = get_genera_plot(
    effective_df = vancouver_df, 
    subtitle = 'Least Common Vancouver Tree Genera',
    most_common = False
)

base_genera_plot = (most_common_plot | least_common_plot) 

genera_plot = base_genera_plot \
.configure_legend(
  orient = 'right',
  titleFontSize = 15,
  labelFontSize = 12
).configure_axis(
    labelFontSize = 10, titleFontSize = 15
).configure_mark(
  stroke = 'black',
  strokeOpacity = 1,
  strokeWidth = 0.8
).configure_axis(
    labelFontSize = 10, titleFontSize = 15
).configure_title(
    fontSize = 25
)
display(genera_plot)

From `Figure 1` :

• The 10 most common tree genera amount to ~74.96% of the total trees in Vancouver.
  • It's noticable that greater than 45% of trees are either Acer or Prunus.
• The 10 least common tree genera amount to ~0.22% of the total trees in Vancouver.
  • The 9 least common tree genera have only 1 tree in Vancouver, while the 10th least common tree genera has 2 trees in Vancouver.
• We might want to look into what other features these trees tend to share.

Q2 : Where are trees located in Vancouver?

Let's bin the latitude and longitude coordinates in a heatmap to visualize the tree density within a given area.

base_coordinates_plot = alt.Chart(
    vancouver_df,
    title = alt.TitleParams(
        text = f'Figure {fig_num} : Location of Vancouver Trees', 
        subtitle = ['Latitude and Longitude Heatmap'],
        anchor = 'start', fontSize = 25, subtitleFontSize = 20
    )
).mark_bar().encode(
    x = alt.X('latitude:Q', title = 'Latitude', bin = alt.Bin(maxbins = 15)),
    y = alt.Y('longitude:Q', title = 'Longitude', bin = alt.Bin(maxbins = 15)),
    color = alt.Color(
        'count():Q', scale = alt.Scale(scheme = 'viridis', reverse = True),
        legend = alt.Legend(
            title = 'Number of Trees', 
            titleFontSize = 14, labelFontSize = 12
        ),
    ),
    tooltip = [alt.Tooltip('count():Q', title = 'Number of Trees')]
).properties(
    width = 600, height = 500
)

coordinates_plot = base_coordinates_plot \
.configure_mark(
  stroke = 'black',
  strokeOpacity = 1,
  strokeWidth = 1.25,
).configure_axis(
    labelFontSize = 15, titleFontSize = 17.5
).configure_title(
    fontSize = 25
)

fig_num += 1
display(coordinates_plot)

From `Figure 2` :

• We observe that trees are generally distributed rather evenly.
• It's noticable that there are 230 trees within 49.250 <= latitude <= 29.260 and -123.120 <= longitude <= -123.100.
• There are fewer trees located around the edges of the map, with the exception of 49.220 <= latitude <= 49.290 and -123.040 <= longitude <= -123.020.

Q3 : What Sizes are Vancouver Trees? Is there a Relationship Between Diameter and Height Range ID?

Let's plot the diameter and height_range_id columns to visualize the relationship between the two properties. This might act as a proxy for determining whether trees occupying a greater area also tend to be taller.

base_sizes_heatmap = alt.Chart(
    vancouver_df
).mark_circle().encode(
    x = alt.X(f'diameter:Q', title = 'Diameter', bin = alt.Bin(maxbins = 15)),
    y = alt.Y(f'height_range_id:Q', title = 'Height Range Id', bin = alt.Bin(maxbins = 15)),
    color = alt.Color(
        'count():Q', scale = alt.Scale(
            scheme = 'viridis', reverse = True,
        ),
        legend = alt.Legend(
            title = 'Number of Trees', 
            titleFontSize = 14, labelFontSize = 12,
            orient = 'right', direction = 'vertical'
        ),
    ),
    size = alt.Size('count():Q'), 
    tooltip = [alt.Tooltip('count():Q', title = 'Number of Trees')]
).properties(
  title = alt.TitleParams(
    text = f'Figure {fig_num} : Vancouver Tree Size Dimensions', 
    subtitle = ['Relationship Between Diameter and Height Range ID'],
    anchor = 'start', fontSize = 25, subtitleFontSize = 20
  ), width = 600, height = 500
)

sizes_heatmap = base_sizes_heatmap \
.configure_mark(
  stroke = 'black',
  strokeOpacity = 1,
  strokeWidth = 0.5
).configure_axis(
    labelFontSize = 15, titleFontSize = 17.5
)

fig_num += 1
display(sizes_heatmap)

From `Figure 3` :

• Trees tend towards lower diameter values and 5 <= height_range_id <= 10.
  • There is a slight positive relationship between diameter and height_range_id where 0 <= diameter <= 25 and 1.0 <= height_range_id <= 5.0.
    • More trees seem to tend towards having feature values in the lower bins of this domain.
  • There are 959 trees with 0 <= diameter <= 5 and 1.0 <= height_range_id <= 2.0.

Q4 : What neighborhoods have the largest trees? What about the smallest trees?

Let's look at the breakdown of this data for both diameter and height_range_id by neighborhood_name.

base_neighbourhoods_plot = alt.Chart(
    vancouver_df, 
    width = 300, height = 350
).mark_boxplot().encode(
    x = alt.X(alt.repeat(), type = 'quantitative'),
    y = alt.Y('neighbourhood_name:N', title = 'Neighbourhoods'),
).repeat(
    ['diameter', 'height_range_id'],
    columns = 3
).properties(
    title = alt.TitleParams(
        text = f'Figure {fig_num} : Vancouver Tree Sizes in Different Neighbourhoods', 
        subtitle = ['Diameters and Height Range ID Distributions'],
        anchor = 'start', fontSize = 25, subtitleFontSize = 20
    )
)

neighbourhoods_plot = base_neighbourhoods_plot \
.configure_mark(
  stroke = 'black',
  strokeOpacity = 1,
  strokeWidth = 0.5
).configure_axis(
    labelFontSize = 12, titleFontSize = 15
).configure_title(
    fontSize = 10
)

fig_num += 1
display(neighbourhoods_plot)

From `Figure 4` :

• For diameter :
  • Most regions tend to have ~10 <= median diameter <= ~15
  • All regions have a 75th percentile diameter <= 24.
  • The Downtown region has a lower range of 4 <= diameter <= 10 between 25th and 75th percentiles.
• For height_range_id :
  • There seem to be 2 buckets of neighbourhood_names :
    • Have a higher 75th percentile >= 4
    • Have a lower 75th percentile ~3.

Q5 : How did tree sizes change by decade?

base_decades_plot = alt.Chart(
    vancouver_df, 
    width = 300, height = 350
).mark_area(opacity = 0.5).encode(
    x = alt.X(alt.repeat(), type = 'quantitative', bin = alt.Bin(maxbins = 15)),
    y = alt.Y('count():Q', title = 'Number of Trees', stack = None),
    color = alt.Color(
        'decade_planted:O', scale = alt.Scale(
            scheme = 'tableau10', reverse = False,
        ),
        legend = alt.Legend(
            title = 'Decade Planted', 
            titleFontSize = 14, labelFontSize = 12
        ),
    )
).repeat(
    ['diameter', 'height_range_id'], columns = 2
).properties(
    title = alt.TitleParams(
        text = f'Figure {fig_num} : Vancouver Tree Sizes in Different Decades', 
        subtitle = ['Diameters and Height Range ID Distributions'],
        anchor = 'start', fontSize = 25, subtitleFontSize = 20
    )
)

decades_plot = base_decades_plot \
.configure_mark(
  stroke = 'black',
  strokeOpacity = 1,
  strokeWidth = 0.8
).configure_axis(
    labelFontSize = 12, titleFontSize = 15
).configure_title(
    fontSize = 10
)

fig_num += 1
display(decades_plot)

From `Figure 5` :

• There are a great number of unidentified date_planted and decade_planted values.
  • These trees appear to have distributions with higher values for diameter and height_range_id.
• For the identified decade_planted values, it seems that :
  • The 2000s and 2010s have a great number of trees with lower values for diameter and height_range_id.
  • The 1980s and 1990s have a similar number of trees with relatively higher values for diameter and height_range_id.

Further Questions

I would like to explore the data in these charts when filtered for criteria including :

• neighbourhood_names with the most trees
• most common genus_names
• decade_planted across the dataset

A few questions start to emerge when looking at data for the columns we've considered for size, as well as trends over time.

• Do trees of the same genus_name have similar numerical features?
• Do trees with the same neighbourhood_name tend to have the same genus_names?
• Where are more trees being planted over time?
• Has the tree density by latitude and longitude changed over time?

Interactive Dashboard

Let's create a dashboard from the visuals above in order to start investigating these questions.

neighbourhoods_select = alt.selection_single(
    fields = ['neighbourhood_name'],
    bind = {
        'neighbourhood_name' : alt.binding_select(
            name = 'Neighbourhoods', 
            options = list(
                vancouver_df.groupby('neighbourhood_name')['genus_name'] \
                    .agg('count').sort_values(ascending = False) \
                    .reset_index()['neighbourhood_name']
            )[:10]
        )
    }
)   

decades_select = alt.selection_single(
    fields = ['decade_planted'],
    bind = {
        'decade_planted' : alt.binding_radio(
            name = 'Decades', 
            options = sorted([decade for decade in vancouver_df['decade_planted'].unique() if decade == decade])
        )
    }
)

genus_select = alt.selection_multi(fields=['genus_name'])

coordinates_plot = base_coordinates_plot \
.add_selection(
    decades_select
).add_selection(
    neighbourhoods_select
).add_selection(
    genus_select
).transform_filter(
    decades_select
).transform_filter(
    neighbourhoods_select
).transform_filter(
    genus_select
)

coordinates_plot.title.text = coordinates_plot.title.text.split(' : ', 1)[-1]

most_common_plot = most_common_plot.encode(
    opacity = alt.condition(genus_select, alt.value(1), alt.value(0.2)),
    tooltip = [
        alt.Tooltip('count():Q', title = 'Number of Trees')
    ]
).properties(
    width = 600, height = 500
).add_selection(
    genus_select
).transform_filter(
    decades_select
).transform_filter(
    neighbourhoods_select
)

most_common_plot.title.text = 'Most Common Vancouver Tree Genera'
most_common_plot.title.subtitle = 'Number of Trees Planted'

sizes_heatmap = base_sizes_heatmap \
.properties(
    width = 350, height = 350
).transform_filter(
    neighbourhoods_select
).transform_filter(
    genus_select
)  

sizes_heatmap.title.text = sizes_heatmap.title.text.split(' : ', 1)[-1]

decades_plot = base_decades_plot \
.transform_filter(
    decades_select
).transform_filter(
    neighbourhoods_select
).transform_filter(
    genus_select
)  

decades_plot.title.text = decades_plot.title.text.split(' : ', 1)[-1]

trees_dashboard = (
  (coordinates_plot | most_common_plot).resolve_scale(
    color = 'independent', size = 'independent'
  ) & (sizes_heatmap | decades_plot).resolve_scale(
    color = 'independent', size = 'independent'
  )
).configure_mark(
  stroke = 'black', strokeOpacity = 1, strokeWidth = 0.5
).configure_axis(
    labelFontSize = 15, titleFontSize = 17.5
).properties(
    title = alt.TitleParams(
        text = f'Trees in Vancouver Metropolitan Area', fontSize = 65, anchor = 'middle'
    )
)

display(trees_dashboard)

Visualizations

Choices

In order to visualize the density of data points, we have used both a traditional heatmap and circle plot of varying colors and sizes. The viridis color scheme enables the clear distinction of changes in density, complemented by a legend.

We have also used a histogram to visualize the number_of_trees per genus_name in the effective dataset. This acts as a simple means of displaying the breakdown of tree genera, while also acting as a dashboard filter.

To compare the diameter and height_range_id distributions, we have layered the data by decade_planted in a translucent area chart. Since we are implementing color to highlight the nominal decade_planted feature here, we are using the tableau10 color scheme.

Potential Improvements

When arranged in a dashboard, the heatmap and circle plot are not aligned. This may be visually jarring and could be improved through ensuring consistency of height and width.

Another improvement would be to ensure that axes are fixed for decade_planted filter selections in the area chart. This filter can be used to help remove unnecessary decade_planted data. This would cause easier visual transitions on the area chart as we look at effective decade_planted values. Consequently, the area chart would also be more accomodating to users with visual deficiencies.

Discussion

We have intentionally decided to visualize the charts in a dashboard prior to our final discussion. This enables us to answer the subsequent questions which arose from our initial analysis.

Summary

• Across all neighbourhood_name, decade_planted, and genus_name values, we observe that :
  • The fields diameter and height_range_id have a relatively stronger positive correlation where there are lower values for both features.
  • There are a great number of unidentified date_planted and decade_planted values.
    • These trees have distributions with higher values for diameter and height_range_id.
    • For identified decade_planted values, it seems the diameter and height_range_id are decreasing post 2000s.
  • Trees are generally distributed rather evenly across the latitude and longitude coordinates, however the density is greater in the center of the map.
  • The 10 most common tree genera amount to ~74.96% of the total trees in Vancouver.
    • It's noticable that greater than 45% of trees are either Acer or Prunus.

Tree Genera

• Considering the dominant Acer and Prunus genus_name :
  • The Acer trees tend to have lower diameter and height_range_id values and exhibit a positive relationship similar to the entire dataset.
    • The Acer trees are clearly exhibit decreasing diameter and height_range_id values over time.
  • The Prunus trees have a similar relationship, but there is a cluster of 10 <= diameter <= 20 and 2.0 <= >height_range_id <= 3.5.
    • There are a great deal of unidentified date_planted and decade_planted values for Prunus trees.

Neighbourhoods

• The most common genus_name are relatively present in all of the high-density neighbourhoods.
  • Either Acer or Prunus are the most common genus_name.
  • Dunbar-Southlands, Sunset, Victoria-Fraserview, and Marpole have a greater number of Prunus trees than Acer trees.
    • There are ~2 times as many Prunus trees than Acer trees in Victoria-Fraserview.

Location

• In the 1990s, there were 25 trees planted at the top edge of the map where 49.230 <= latitude <= 49.240 and -123.040 <= longitude <= -123.020.
• There was another cluster of 71 trees planted in the 1990s where 49.210 <= latitude <= 49.230 and -123.140 <= longitude <= -123.100.
  • In the 2000s, there were ~130 trees planted where 49.210 <= latitude <= 49.220 and -123.120 <= longitude <= -123.060.
  • In the 2010s, more trees were planted where 49.210 <= latitude <= 49.220 and -123.100 <= longitude <= -123.080.
  • Trees are consistently planted in this general vicinity.

Unanswered Questions

In order to understand where trees are being planted over time, it would make sense to visualize the time-series data of number_of_trees and compare this for different neighbourhood_name values.</br> Another question which arises from the dashboard is whether neighbourhood_name or latitude/longitude coordinates are the better indicator for tree location. These could be better explored in a subsequent analysis. We could visualize the data in a map of Vancouver to get a clear understanding.

References

The data were obtained from The city of Vancouver's Open Data Portal and follows an Open Government Licence – Vancouver.

These additional resources provide the theory and code segments for the Analysis Report in this notebook :

• Data Visualization
• Machine Learning Final Project
• Python for Data Science Final Project